Current radiological practice is generally qualitative, e.g., in cancer care, “a peripherally enhancing spiculated mass in the lower left lobe.” When radiological practice is quantitative, measurements are commonly limited to dimensional measurements of tumor size via one-dimensional (Response Evaluation Criteria In Solid Tumors [RECIST]) or two-dimensional (2D) (World Health Organization) long-axis measures. These quantitative measures do not reflect the complexity of tumor morphology or behavior, nor, in many cases, are changes in these measures predictive of therapeutic benefit. When additional quantitative measures are obtained, they generally average values over an entire region of interest (ROI).
Radiology is however moving towards more precise and more quantitative information extraction. Thus, radiological scans are moving from “imaging” modalities to “measurement” modalities, aided by tremendous increases in computational power and intelligent software. For example, algorithms exist to reliably segment regions of interest from radiological scans and extract quantitative descriptive features. There are also efforts to develop a standardized lexicon for describing lesions or tumors and to include these descriptors via annotated image markup into quantitative, mineable data. However, these approaches do not completely cover the range of quantitative features that can be extracted from images, such as texture, shape or margin gradients.